HistomicsML2.0: Fast interactive machine learning for whole slide imaging data
Sanghoon Lee, Mohamed Amgad, Deepak R. Chittajallu, Matt McCormick, Brian P Pollack, Habiba Elfandy, Hagar Hussein, David A Gutman, Lee AD Cooper
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Extracting quantitative phenotypic information from whole-slide images presents significant challenges for investigators who are not experienced in developing image analysis algorithms. We present new software that enables rapid learn-by-example training of machine learning classifiers for detection of histologic patterns in whole-slide imaging datasets. HistomicsML2.0 uses convolutional networks to be readily adaptable to a variety of applications, provides a web-based user interface, and is available as a software container to simplify deployment.